Investigations of Water Transport in Shale Reservoir with Dual-Wettability by Using Monte Carlo Method.

How liquids transport in the shale system has been the focus because of fracturing fluid loss. In this study, a single-nanopore model is established for liquid transport in shale while considering the slip effect and effective viscosity of confined fluids. Then, the fractal Monte Carlo (FMC) model is proposed to upscale the single-pore model into shale porous media. The effects of different transport mechanisms, shale wettability, and pore characteristic parameters on confined liquid flow in shale rock are investigated. Results show that FMC permeabilities are 2-3 orders of magnitude larger than intrinsic and slip-corrected permeabilities in organic matter. However, the slip effect and effective viscosity have little influence on water flow in inorganic matter. With the contact angle of organic pore (θom) increasing and contact angle of inorganic pore (θin) decreasing, the effective permeability of the whole shale matrix grows in number. The enhancement factor in the situation of θom = 170° and θin = 20° is 4 orders of magnitude larger than the case of θom = 130° and θin = 40°, although the close effective macroscopic contact angle (θeff = 80°) occurs in these two cases, which indicates that shale microscopic wettability has a significant impact on the confined liquid transport. Moreover, with the increase of porosity and maximum pore diameters, shale permeability increases rapidly, but the enhancement factor has the opposite trend. Compared with the tiny impact of the variance of minimum inorganic pore diameters, minimum organic pore diameters have more significant impacts on liquid flow in shale systems, and the enhancement factor also rapidly increases up to 30 times for the case of 0.5 nm because of the strong slip effect.

Prediction Model for Tight Gas Wells with Time-Dependent Mechanism and Stress Sensitivity Effect.

In the production process of tight gas wells, reservoir fluid distribution and gas-water relative permeability vary with time. However, traditional models fail to handle the time-dependent mechanism and stress sensitivity effect in the reservoir, leading to significant errors in the dynamic analysis results. To address this issue, this article presents a prediction model for fractured well production in tight gas reservoirs. It is based on a three-dimensional embedded discrete fracture model (EDFM), which considers the influences of the time-dependent mechanism and stress-dependent reservoir permeability. Transient flow equations are treated by using the finite volume method to obtain the solution of the model. The accuracy and reliability of the model are verified by comparison with the results of the commercial simulator Eclipse and the field application. Based on the model's solution, this study emphasizes the analysis of the impact of the time-dependent mechanism and reservoir stress sensitivity on gas well productivity. Simulation results show that the time-dependent relative permeability curve can decrease the level of irreducible water saturation and promote the migration of irreducible water, resulting in an increase in water permeability and a decrease in gas permeability. This effect will reduce the period of stable gas production and increase the level of water production. Besides, reservoir stress sensitivity will reduce daily water production and accelerate gas well decline. It is necessary to control the production pressure difference reasonably during the production process to effectively reduce the negative impact of stress sensitivity effects. The results indicate that when the relative permeability curve and the reservoir permeability are constant, the real gas production capacity of the reservoir will be strengthened. The application of field case studies shows that the theoretical model exhibits stronger adaptability, achieves better fitting results, and can guide the compilation and adjustment of development plans for water-bearing tight gas reservoirs. These findings provide insights into understanding the effects of the time-dependent mechanism on gas production rates in tight gas reservoirs. Furthermore, this study offers useful guidance for the prediction of field-scale gas production.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Diagnostic accuracy of low-dose 256-slice multi-detector coronary CT angiography using iterative reconstruction in patients with suspected coronary artery disease.

OBJECTIVES: To evaluate the accuracy of low-dose coronary CTA with iterative reconstruction (IR) in the diagnosis of coronary artery disease (CAD) in patients with suspected CAD. METHODS: Ninety-six patients with suspected CAD underwent low-dose prospective electrocardiogram-gated coronary CTA, with images reconstructed using IR. Image quality (IQ) of coronary segments were graded on a 4-point scale (4, excellent; 1, non-diagnostic). With invasive coronary angiography (ICA) considered the "gold standard", the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of coronary CTA were calculated on segment-, vessel- and patient-based levels. The patient data were divided into two groups (Agatston scores of ≥ 400 and <400). The differences in diagnostic performance between the two groups were tested. RESULTS: Diagnostic image quality was found in 98.1 % (1,232/1,256) of segments. The sensitivity, specificity, PPV, NPV and accuracy were 90.8 %, 95.3 %, 81.8 %, 97.8 % and 94.3 % (segment-based) and 97.2 %, 83.3 %, 94.6 %, 90.9 % and 93.8 % (patient-based). Significant differences between the two groups were seen in specificity, PPV and accuracy (92.1 % vs. 97.9 %, 76.0 % vs. 86.7 %, 91.7 % vs. 96.6 %, P < 0.05; segment-based). The average effective dose was 1.30 ± 0.15 mSv. CONCLUSION: Low-dose prospective coronary CTA with IR can acquire satisfactory image quality and show high diagnostic accuracy in patients with suspected CAD; however, blooming continues to pose a challenge in severely calcified segments. KEY POINTS: • Coronary artery disease (CAD) is increasingly investigated using coronary CTA. • The iterative reconstruction (IR) algorithm is promising in decreasing radiation doses. • Low-dose prospective coronary CTA with IR can acquire satisfactory image quality. • Low-dose prospective coronary CTA with IR can show high diagnostic accuracy.